Station keeping is an essential maneuver for Autonomous Surface Vehicles (ASVs), mainly when used in confined spaces, to carry out surveys that require the ASV to keep its position or in collaboration with other vehicles where the relative position has an impact over the mission. However, this maneuver can become challenging for classic feedback controllers due to the need for an accurate model of the ASV dynamics and the environmental disturbances. This work proposes a Model Predictive Controller using Neural Network Simulation Error Minimization (NNSEM-MPC) to accurately predict the dynamics of the ASV under wind disturbances. The performance of the proposed scheme under wind disturbances is tested and compared against other controllers in simulation, using the Robotics Operating System (ROS) and the multipurpose simulation environment Gazebo. A set of six tests were conducted by combining two wind speeds (3 m/s and 6 m/s) and three wind directions (0$^\circ$, 90$^\circ$, and 180$^\circ$). The simulation results clearly show the advantage of the NNSEM-MPC over the following methods: backstepping controller, sliding mode controller, simplified dynamics MPC (SD-MPC), neural ordinary differential equation MPC (NODE-MPC), and knowledge-based NODE MPC (KNODE-MPC). The proposed NNSEM-MPC approach performs better than the rest in 4 out of the 6 test conditions, and it is the second best in the 2 remaining test cases, reducing the mean position and heading error by at least 31\% and 46\% respectively across all the test cases. In terms of execution speed, the proposed NNSEM-MPC is at least 36\% faster than the rest of the MPC controllers. The field experiments on two different ASV platforms showed that ASVs can effectively keep the station utilizing the proposed method, with a position error as low as $1.68$ m and a heading error as low as $6.14^{\circ}$ within time windows of at least $150$s.

使用神经网络模拟误差最小化的模型预测控制器(NNSEM-MPC)来准确预测在风扰动下自主水面航行器(ASV)的动态特性，并通过模拟实验比较了该方法与其他控制器的性能。实验结果表明NNSEM-MPC在4个测试条件中表现优于其他方法，在其余2个测试用例中也是第二好的，减少了至少31%的位置误差和46%的航向误差；在执行速度方面，NNSEM-MPC至少比其他MPC控制器快36%。通过两个不同的ASV平台进行的实地实验表明，ASV可以有效地利用该方法保持位置和航向，位置误差最低达1.68米，航向误差最低达6.14度，持续至少150秒。

利用神经网络的模拟误差最小化模型预测控制实现ASV在风扰动下的保持定位